Author: Ganna Pogrebna

Big Data, Consumer Choice & Context

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Big Data, Consumer Choice & Context

 

Big Data, Consumer Choice & Context

Consumer data is a valuable asset in the current age of data with “smart things” (sensors) delivering large amounts of information to consumers and businesses. According to IBM, in 2012 more than 2.5 exabytes (2.5 billion gigabytes) of data was generated daily. By 2015 this number has grown and, according to forecasts, will continue to grow to 40,000 exabytes by 2020. Under these circumstances, businesses develop innovative techniques to extract and analyse data “on the fly” in order to create quick value propositions for the consumers. The availability of large masses of data catalyses the rise of the domain of data-driven business models (DDBM) which looks at how the data can be used in order to develop new and improve existing business modelling mechanisms.

Yet, the creation of meaningful analytical tools for DDBM is complicated not only because of the volume of the data but also because of the complexity of human decision processes and the way these processes are reflected in the data. Particularly, household consumption data shows that people who shop in the same store may opt for different products and/or brands of products. For example, when making grocery purchases, consumers often tend to alternate brands of products they choose. This is one of the reasons why current online systems developed by some providers such as, e.g., Amazon, which suggest products and services to users and which are intended to nudge users to purchase suggested services and goods, have not gained much popularity.

One of the main disadvantages of the currently available purchasing data is that even though it allows analysts to observe consumer choices as well as providing them with useful demographic information about consumers; it is hard to tell whether observed choices are a result of consumer true preferences or merely a product of noise in these preferences. Analytics is particularly complicated for cases when consumers opt for products and services from different brands in different environments. Under these circumstances, it is important to not only pay attention to the models which help us analyse the data generated by consumer choices, but also to the types of data used for the analysis.

Read full blogpost on the HARRIET website…

 

The next phase of the HAT has begun with HARRIET, an RCUK-funded project which will see the HAT platform being deployed into real households. Partnering with Birmingham City Council, HARRIET will develop the HAT Resource Integration and Enabling Tool that will assist individuals to better understand their household consumption behaviour and make ‘smarter’ decisions to plan and live better lives based on their own data stored on the HAT. Find out more from the HARRIET Project website, which currently features new blogposts on Smart Cities, Personal Data, and Smart Things and Data Analytics.

 

Who Should Regulate Privacy?

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Who Should Regulate Privacy?

Earlier this month, I attended a conference on Household Financial Decision Making and Behaviour in Nottingham. Held between May 6-8, it was organised by the Network for Integrated Behavioural Science, a research initiative between the University of Warwick, University of Nottingham, University of East Anglia and funded by the Economic and Social Research Council (ESRC).

The conference concentrated on financial as well as consumption decisions observed in markets, with many talks devoted to the extent to which modern theories of decision-making can capture these decisions.One of the keynote addresses was delivered by George Loewenstein of Carnegie Mellon University, who is renowned for his work as a leader in behavioural economics.

Loewenstein spoke about Behavioural Economics and Privacy in the new digital economy. He referred to a series of decision-making experiments conducted by himself and his co-authors, which demonstrated how individuals recklessly endanger their own privacy by revealing sensitive information together with identifying data without considering the consequences. For example, an individual may post compromising photos on Facebook, not thinking that her employer may be regularly monitoring employees’ accounts on social media portals. Loewenstein concluded that individuals generally could not be trusted with their own privacy and that the government should step in and regulate privacy issues relating to online behaviour. In particular, he suggested that a set of rules should be developed for social media service providers such as Facebook, Twitter, YouTube, Google+, etc. to ensure their customers’ security.

While the issue of privacy and cyber security of social media is very topical, I am not convinced that governmental regulation can provide a cure. We already see interesting trends which suggest that consumers are becoming more and more aware of such privacy issues, and they choose services with transparent protocols. For example, the user base of Facebook is aging, with younger people switching to services like Twitter and Instagram.

In my opinion, this not only reflects the fact that kids do not want to be present on networks used by their parents but also shows that consumers of the future prefer social media portals where they can show the world what they see (Instagram) rather than letting the world see them (Facebook). My sister, who is 7 years my junior, for example, explains that she would rather use Instagram where everyone can see everyone else rather than use Facebook where even after setting the highest access restrictions, your information can go viral should your closest Facebook friends decide to tag your private information.

There is no doubt that, considering the modern business models which exist in the markets for information, private companies may fail miserably in protecting customer privacy. The brightest example in the last few weeks is the case of Snapchat deceiving their users into believing that the information they exchanged via the Snapchat services was almost instantly deleted.

Yet, there is no guarantee that the government can do a better job! For example, the UK government has traded school data, NHS data and even taxpayer data with private companies, sparking debates about the effectiveness of any type of governmental regulation of information storage and transfers.

I believe that our understanding of privacy and cyber security will undergo serious transformation in the next few years, with new business models being developed around markets for information. This is a particular issue that we are also looking at in the HAT project.

However – and I might be alone on this — if given a choice between deciding for myself what information to share in a free market or engaging in information exchange in a market where government acts as some sort of Big Brother, I would choose self-regulated markets every time!

Wouldn’t you?

 

Note: This blogpost originally appeared on the BIG Blog